Review of Quantitative Finance and Accounting

, Volume 13, Issue 4, pp 323–345

Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology

  • Emel Kahya
  • Panayiotis Theodossiou
Article

Abstract

The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit.

financial distress models linear discriminant analysis logit model non-stationary financial ratios time-series CUSUM vector autoregressive process 

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Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Emel Kahya
    • 1
  • Panayiotis Theodossiou
    • 2
  1. 1.School of BusinessRutgers UniversityCamden
  2. 2.School of BusinessRutgers UniversityCamden

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